Session:「Algorithms in (Social) Practice」

論文アブストラクト：
Algorithms influence most of our daily activities, decisions, and they guide our behaviors. It has been argued that algorithms even have a direct impact on democratic societies. Human - Computer Interaction research needs to develop analytical tools for describing the interaction with, and experience of algorithms. Based on user participatory workshops focused on scrutinizing Facebook's newsfeed, an algorithm-influenced social media, we propose the concept of Algorithmic Experience (AX) as an analytic framing for making the interaction with and experience of algorithms explicit. Connecting it to design, we articulate five functional categories of AX that are particularly important to cater for in social media: profiling transparency and management, algorithmic awareness and control, and selective algorithmic memory.

Communicating Algorithmic Process in Online Behavioral Advertising

論文アブストラクト：
Advertisers develop algorithms to select the most relevant advertisements for users. However, the opacity of these algorithms, along with their potential for violating user privacy, has decreased user trust and preference in behavioral advertising. To mitigate this, advertisers have started to communicate algorithmic processes in behavioral advertising. However, how revealing parts of the algorithmic process affects users' perceptions towards ads and platforms is still an open question. To investigate this, we exposed 32 users to why an ad is shown to them, what advertising algorithms infer about them, and how advertisers use this information. Users preferred interpretable, non-creepy explanations about why an ad is presented, along with a recognizable link to their identity. We further found that exposing users to their algorithmically-derived attributes led to algorithm disillusionment---users found that advertising algorithms they thought were perfect were far from it. We propose design implications to effectively communicate information about advertising algorithms.

Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

論文アブストラクト：
Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions-like taxation, justice, and child protection-are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning-absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the 'street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.

A Qualitative Exploration of Perceptions of Algorithmic Fairness

論文アブストラクト：
Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases, algorithmic systems may be more or less favorable to certain groups or individuals, sparking substantial discussion of algorithmic fairness in public policy circles, academia, and the press. We broaden this discussion by exploring how members of potentially affected communities feel about algorithmic fairness. We conducted workshops and interviews with 44 participants from several populations traditionally marginalized by categories of race or class in the United States. While the concept of algorithmic fairness was largely unfamiliar, learning about algorithmic (un)fairness elicited negative feelings that connect to current national discussions about racial injustice and economic inequality. In addition to their concerns about potential harms to themselves and society, participants also indicated that algorithmic fairness (or lack thereof) could substantially affect their trust in a company or product.